Every topic on the official USAAIO syllabus, organized into 8 teachable modules. Round 1 covers Modules 0–5; Round 2 covers everything. 官方考纲拆分为 8 个模块。第一轮覆盖 0–5，第二轮覆盖全部。
| # | Module | Round | Official topics covered |
|---|---|---|---|
| 0 | Python & Data Tooling | R1 | Python, NumPy, pandas, matplotlib.pyplot, seaborn; Markdown in Google Colab |
| 1 | Math Foundations for AI | R1 | Linear algebra (affine transforms, matrix decompositions, eigenvalues/eigenvectors); probability & statistics (Bayes' rule, Hoeffding's inequality); multivariable derivatives; convex optimization (gradient descent, duality) |
| 2 | Supervised Learning | R1 | Linear & logistic regression, SVM, decision trees, kNN, ensemble learning, bias-variance tradeoff, cross-validation, loss functions |
| 3 | Unsupervised Learning | R1 | k-means clustering, principal component analysis (PCA) |
| 4 | Deep Learning Foundations | R1 | Multi-layer perceptron; essential layers (affine, batch norm, dropout); forward & backpropagation by hand; PyTorch |
| 5 | Convolutional Neural Networks | R1 | CNN basics, image tasks (Round 1 intro level) |
| 6 | Transformers & NLP | R2 | Attention, transformer architecture, vision transformers, GNNs; tokenization, word embeddings, pre-training, fine-tuning |
| 7 | Computer Vision & Generative AI | R2 | Object detection, UNet, autoencoder, VAE, GAN, denoising diffusion (DDPM), stable diffusion |